Comparison of machine learning-based book comments sentiment analysis for constructing recommendation system

Abstract

The exponential growth in the volume of books available, along with the proliferation of online platforms, has made it increasingly challenging for readers to find books tailored to their interests. This research paper aims to address this challenge by developing an effective book recommendation system based on user reviews and ratings, primarily drawn from Amazon’s dataset covering the period from May 1996 to July 2014. Using a K-Nearest Neighbors (KNN) algorithm and a Random Forest baseline model, the study focuses on comparative analyses in terms of Mean Squared Error (MSE) and computational costs. The KNN model outperformed the baseline model with a lower MSE of 0.15 compared to 0.38 and proved to be computationally less exacting. While the KNN model is currently the more tenable option for deployment, the paper posits that an ensemble approach may offer a more robust solution. Future work aims to include sentiment analysis, explore other recommendation algorithms, and make use of more advanced evaluation metrics. This study provides a foundation for the advancement of book recommendation systems, offering insights into their efficiency and effectiveness.